Verifying data consistency

ABSTRACT

A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.

BACKGROUND

The present invention generally relates to verifying data consistency,and more particularly verifying data consistency between update-in-placedata structures and append-only data structures.

Emerging processing solutions provide a platform for curation andanalysis of massive amounts of live (continuously updated) data byintegrating big data processing with recovery log capture technologyassociated with update-in-place data structures, such as a relationaldatabase management systems (RDBMS). Big data refers to a massive amountof structured and/or unstructured data that is too large to process withtraditional database techniques, e.g., a query-in-serial. Big dataplatforms may use distributed storage architecture (e.g., a distributedfile system) and a distributed processing architecture. To supportqueries over a temporally complete, continuously updated history ofRDBMS change data, processing solutions may continuously append thechange history into big data targets, such as an append-only datastructure (e.g., a log file/table stored in a distributed file systemassociated with a big data platform). However, data changes due tofaulty replication processes, data corruptions, operator errors, and thelike, may occur at the side of the update-in-place data structure or theside associated with the append-only data structure.

SUMMARY

According to one embodiment, a method for verifying data consistencybetween update-in-place data structures and append-only data structurescontaining change histories associated with the update-in-place datastructures is provided. The method may include loading data from anupdate-in-place data structure to a first set of hash buckets in aprocessing platform, loading data from append-only data structures to asecond set of hash buckets in the processing platform, performing abucket-level comparison between the data in the first set of hashbuckets and the data in the second set of has buckets, and generating areport based on the bucket-level comparison.

According to another embodiment, a computer program product forverifying data consistency between update-in-place data structures andappend-only data structures containing change histories associated withthe update-in-place data structures is provided. The computer programproduct may include at least one computer readable non-transitorystorage medium having computer readable program instructions forexecution by a processor. The computer readable program instructions mayinclude instructions for loading data from an update-in-place datastructure to a first set of hash buckets in a processing platform,loading data from append-only data structures to a second set of hashbuckets in the processing platform, performing a bucket-level comparisonbetween the data in the first set of hash buckets and the data in thesecond set of has buckets, and generating a report based on thebucket-level comparison.

According to another embodiment, a computer system for verifying dataconsistency between update-in-place data structures and append-only datastructures containing change histories associated with theupdate-in-place data structures is provided. The system may include atleast one processing unit, at least one computer readable memory, atleast one computer readable tangible, non-transitory storage medium, andprogram instructions stored on the at least one computer readabletangible, non-transitory storage medium for execution by the at leastone processing unit via the at least one computer readable memory. Theprogram instructions may include instructions for loading data from anupdate-in-place data structure to a first set of hash buckets in aprocessing platform, loading data from append-only data structures to asecond set of hash buckets in the processing platform, performing abucket-level comparison between the data in the first set of hashbuckets and the data in the second set of has buckets, and generating areport based on the bucket-level comparison.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description, given by way of example and notintended to limit the invention solely thereto, will best be appreciatedin conjunction with the accompanying drawings, in which:

FIGS. 1A-1C represent exemplary row structures for an update-in-placedata structure, an append-only data structure, and a difference report,respectively, according to embodiments;

FIG. 2 is a flowchart illustrating an exemplary method for verifyingdata consistency between update-in-place data structures and append-onlydata structures containing change histories associated with theupdate-in-place data structures, according to an embodiment;

FIG. 3 is another flowchart illustrating an exemplary method forverifying data consistency between update-in-place data structures andappend-only data structures containing change histories associated withthe update-in-place data structures, according to an embodiment;

FIG. 4 is a block diagram illustrating a computing node, according to anaspect of the invention;

FIG. 5 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 6 depicts abstraction model layers according to an embodiment ofthe present invention.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention. In the drawings, like numbering representslike elements.

DETAILED DESCRIPTION

Various embodiments of the present invention will now be discussed withreference to FIGS. 1-6, like numerals being used for like andcorresponding parts of the various drawings.

According to one embodiment, provided is a method for verifying dataconsistency between update-in-place data structures (e.g., files ortables) and append-only data structures (e.g., files or tables)containing change histories associated with the update-in-place datastructures by loading data from the update-in-place data structures andappend-only data structures into respective hash buckets in a processingplatform (such as a big data platform, e.g., a cluster of machinesrunning a big data processing framework) and performing a bucket levelcomparison between corresponding hash buckets. The data from theupdate-in-place data structures may be loaded to a first set of hashbuckets based on hash values determined by key values associated withthe data in the update-in-place data structures, and the data from theappend-only data structures may be loaded to a second set of hashbuckets based on hash values determined by key values associated withthe data in the append-only data structures. The bucket level comparisonmay be directed to comparing data in buckets associated with the samehash value, i.e., a common hash value. A report, e.g., a differencereport, may be generated based on the bucket-level comparison.

The methods, computer program products, and systems disclosed herein mayenable data consistency verification between an update-in-place datastructure, such as a relational database management system (RDBMS), anddata replicated to a processing platform (e.g., a big data platform) bycomparing RDBMS data to data in append-only data structures containing achange history, which may be stored as log-structured data structures ina big data target (e.g., a distributed file system associated with a bigdata platform). Update-in-place data structures may be continuouslyupdated by various workloads, e.g., in the non-limiting case of onlinetransaction processing (OLTP). Append-only data structures may contain atemporally complete history of change data for the update-in-place datastructures, e.g., RDBMS change data. The change data (e.g., writesets)may be continuously appended to the append-only data structures byreplication software.

Data consistency verification, as disclosed herein, may be performed innear-real time without system downtime, according to an embodiment. Forexample, a system shutdown may not be necessary to perform the dataconsistency verification disclosed herein, according to an embodiment.

The methods, computer program products, and systems disclosed herein mayenable detection of persistent differences (i.e., actual differencesbetween an update-in-place data structure and append-only datastructures) as opposed to transient differences introduced during theverification process caused by asynchronous replication process latency.For example, transient differences may be introduced by in-flighttransactions or rollback transactions committed at the update-in-placedata structure side after the data consistency verification process hascommenced. By detecting and removing transient differences from adifference report, the difference report may inform a reviewer or systemof only actual differences that may require attention or correctiveaction.

FIG. 1A depicts an exemplary source row structure 110 for anupdate-in-place data structure, according to an embodiment. In anembodiment, data stored in the source row structure 110 may bereplicated to a big data target, such as an append-only data structure,which may include a temporally complete history of changes to theupdate-in-place data structure. Source row structure 110 may includesource key columns 111, source non-key columns 112, and source checksumvalues 113.

Source key columns 111 may contain key values associated with rows inthe update-in-place data structure. Each key value may uniquely identifya row in the update-in-place data structure. Source non-key columns 112may include other, remaining columns of data in the update-in-place datastructure. Data in source non-key columns 112 may include filename, datatype, and other attributes. Source checksum values 113 may includerow-based checksum values for the record, i.e., data associated with arow in the update-in-place data structure. It is contemplated thatsource row structure 110 (and the update-in-place data structure) mayinclude additional data and/or information.

In an embodiment, a row structure for a source update-in-place datastructure may not include checksum values for the source data. In suchcase, the checksum values may be calculated during data loading from theupdate-in-place data structure to the processing platform (during dataconsistency verification).

After or during a data change in the update-in-place data structure,replication software may capture the changes by reading a transactionrecovery log, or the update-in-place data structure (e.g., RDBMS) maytrigger a logging process, which may append the changes to correspondingappend-only files. In an embodiment, an update-in-place data structuremay correspond to an append-only file, which may be a temporal file, logstructured file, history file, change file, etc.

FIG. 1B depicts an exemplary target row structure 120 for an append-onlydata structure, according to an embodiment. In an embodiment, datastored in the target row structure 120 may include a temporally completehistory of changes to a source, update-in-place data structure. Targetrow structure 120 may include version values 121, difference type 122,user-column-after-image values 123, and target checksum values 124.

Version values 121 may identify the change sequences of a specificrecord. In an embodiment, version values 121 may be a log sequencenumber (LSN) or transaction commit timestamp (e.g., COMMIT_TIME). TheLSN may uniquely identify a change in an RDBMS recovery log, and the LSNvalue may ascend globally. The COMMIT_TIME may be extracted from thetransaction manager log entry or generated by RDBMS triggers.

Difference type 122 may be a flag indicating the type of operation for aparticular record. Types operations listed in difference type 122 may bean insertion (I), an update (U), or a deletion (D).

User-column-after-image values 123 may contain values that are in thesource column (i.e., in the source, update-in-place data structure)after the data change. For each source column, target row structure 120,may contain a corresponding after-image column. User-column-after-imagevalues 123 stored in the after-image columns may include the samefilename, data type, and other attributes as the corresponding sourcecolumns in target row structure 120. In the case of an update, theafter-image value may reflect the new value of the data that wasupdated. In the case of a deletion, the after-image value may reflectthe value of the data that was deleted. In the case of an insertion, theafter-image value may reflect the value of the data that was inserted.

In an embodiment, a single key value in the source, update-in-place datastructure may correspond to multiple rows in the append-only datastructures, with each row (in the append-only files) corresponding to aspecific version of the single key value.

Target checksum values 124 may include row-based checksum values for thedata, i.e., record, associated with a row in the append-only datastructures.

It is contemplated that target row structure 120 (and the append-onlydata structures) may include additional data and/or information.

In an embodiment, a row structure for an append-only data structure maynot include checksum values for the target data. In such case, thechecksum values may be calculated during data loading from theappend-only data structures to the processing platform (during dataconsistency verification) or during the data comparison process. In anembodiment, the checksum values for the source data and the checksumvalues for the target data are determined using the same function, i.e.,a common function.

FIG. 1C depicts an exemplary report row structure 130 for a differencereport, according to an embodiment. In an embodiment, data from anupdate-in-place data structure (e.g., stored in source row structure110) may be compared to data from append-only data structures (e.g.,stored in target row structure 120) to verify data consistency.Differences between the data may be provided in a report, e.g., adifference report, containing report row structure 130. Report rowstructure 130 may include report key columns 131, report difference type132, and report checksum values 133.

Report key columns 131 may contain key values associated withdifferences listed in the difference report. Each key value may uniquelyidentify a difference in the report. Report difference type 132 may be aflag indicating the type of operation associated with a listeddifference. Types of operations listed in report difference type 132 maybe an insertion (I), an update (U), or a deletion (D).

An insertion (I) may indicate that a current snapshot of the append-onlydata structure does not contain such a key value. An update (U) mayindicate that the non-key columns of the source, update-in-place datastructure (e.g., non-key columns 112) and the corresponding targetcolumns, append-only data structure (e.g., user-column-after-imagevalues 123) do not match. A deletion (D) may indicate that a currentsnapshot of the update-in-place data structure does not contain such akey value.

Report checksum values 133 may include the row-based checksum values forthe data in the update-in-place data structures and the row-basedchecksum values for the append-only data structures.

FIG. 2 illustrates a first flowchart 200 depicting an exemplary methodfor verifying data consistency between update-in-place data structuresand append-only data structures containing change histories associatedwith the update-in-place data structures, according to an embodiment. At202, data from an update-in-place data structure (e.g., RDBMS) may beloaded to a first set of hash buckets in a processing platform, such asa big data platform (e.g., a cluster of machines running a big dataprocessing framework). The data may be fetched or unloaded from theupdate-in-place data structure in an uncommitted read (UR) isolationlevel, which may minimize the impact on the update-in-place datastructure.

In a first aspect, data transmission from the update-in-place datastructure to the big data platform may be implemented by transferringkey values and the row-based checksums from the update-in-place datastructure to the big data platform. In one embodiment, the original datain the update-in-place data structure may be concurrently fetched usingmultiple database connections. In another embodiment, data in aparticular key value range may be fetched in a separate job running inthe big data processing framework. Parallel fetching may be scheduled indifferent nodes or machines within a cluster running the big dataprocessing framework. In another embodiment, less than all availablecolumns of data are transferred, which may reduce the overhead cost ofnetwork transmission. In embodiments where the update-in-place datastructures do not contain row-based checksum values, the checksum valuesmay be calculated using a stored procedure or user-defined function whenthe rows of data are fetched from the update-in-place data structure.

In another aspect, data transmission from the update-in-place datastructure to the big data platform may be implemented by unloading theoriginal data in the update-in-place data structure to an external filewithout the overhead cost of passing structured query language (SQL)statements to the update-in-place data structure (via SQL tools andutilities) or the overhead cost of loading such SQL statements to thebig data platform.

The loaded data (e.g., key values, row-based checksums) may be stored inthe first set of hash buckets in the big data platform based on hashvalues associated with the key values of loaded data. The hash valuesmay be calculated by a hash function, which may be predetermined. In oneembodiment, the rows of loaded data may be evenly distributed among thehash buckets.

In one embodiment, the number of hash buckets is user-defined. Inanother embodiment, the number of buckets is adjustable based on thenumber of rows in the update-in-place data structure.

In one embodiment, the loaded data in each hash bucket is persistent onthe disks associated with the cluster of machines associated with thebig data platform. In another embodiment, the loaded data in each hashbucket is not persistent on the disks associated with the big dataplatform, and in the case of a failure, the data may be reloaded orre-fetched from the update-in-place data structure.

At 204, data from one or more append-only data structures (e.g.,recovery log files) may be loaded to a second set of hash buckets in theprocessing platform. A current or latest snapshot of the append-onlydata structure may be generated and stored in the second set of hashbuckets based on hash values associated with the key values of the datain the append-only data structure. The hash values may be calculated bya hash function, which may be predetermined. In an embodiment, the hashfunction used to calculate the hash values for data from the append-onlydata structure is the same hash function used to calculate the hashvalues for data from the update-in-place data structure, i.e., the hashfunction is common to both sets of calculations.

In an embodiment, the loaded data from the append-only data structuremay be limited to data with a source side COMMIT_TIME that is earlierthan a start time for loading the data from the update-in-place datastructure (see step 202).

In one embodiment, a single update-in-place data structure maycorrespond to multiple append-only data structures. The time domain ofCOMMIT_TIME may be split into multiple successive intervals, and eachinterval may correspond to a separate append-only data structure. It iscontemplated that LSN may similarly be split into multiple successiveversions, and each version may correspond to a separate append-only datastructure. In one embodiment, data from separate append-only datastructures may be loaded in separate jobs running in the big dataprocessing framework. The separate jobs may be distributed to differentnodes within the computing cluster. In one embodiment, assignment of ajob to a particular node is based on the location of the data.

In one embodiment, for each specific key value in an append-only datastructure, the rows with a higher version number may be loaded. In anon-limiting example, an SQL interface may be used to fetch the rows forgenerating the latest snapshot: “SELECT max(VERSION),KEY-user-columns-after-image, CRC, OP FROM append-only-file GROUP BYKEY-user-columns-after-image, CRC, OP.” The non-limiting exemplary querymay be internally converted to multiple jobs by an SQL utility.

In embodiments where the append-only data structures do not containrow-based checksum values, the checksum values may be calculated using astored procedure or user-defined function, which may be the same as (orcommon with) the function used to calculate checksum values in theupdate-in-place data structure. In one embodiment, after completing achecksum calculation, the values of non-key columns may no longer beloaded/stored.

In one embodiment, data representation of user-column-after-image inappend-only data structures may be different from the datarepresentation in the update-in-place data structure. For example, codepages associated with the append-only data structures and theupdate-in-place data structure may be different. An originaltransformation may have been conducted during replication or movement ofdata changes for the update-in-place data structures. In such cases, acorresponding data transformation may be performed prior to calculatingthe row-based checksums.

In one embodiment, loading the data from the append-only data structuresmay be performed in parallel with loading the data from theupdate-in-place data structure.

At 206, a bucket-level comparison may be performed between the data inthe first set of hash buckets (i.e., loaded from the update-in-placedata structure) and the second set of hash buckets (i.e., loaded fromthe append-only data structures).

In one embodiment, rows of data from the append-only data structuresassociated with a deletion (D) operation may be removed from the secondset of hash buckets. Such rows may be removed from the current or latestsnapshot of the append-only data structures. Removal of the rows may beperformed before or during bucket-level comparison.

Hash buckets having the same hash value (or sharing a common hash value)may be compared in the same processing job. In a further embodiment, atleast one hash bucket may be moved to a node where another hash bucketsharing the same hash value is located. In one embodiment, multiplebucket-level comparison jobs are performed in parallel.

At 208, a report (e.g., a difference report) may be generated based onthe bucket-level comparison. In one embodiment, key values may beomitted (or removed) from the difference report when the key value androw-based checksum values between the first and second hash bucketsmatch. In an embodiment, a specific key value may be identified as adifference (in the difference report) when that key's corresponding(update-in-place and append-only) checksum values do not match or thekey value does not exist in either the update-in-place data structure orthe append-only data structures.

In one embodiment, no update occurs at either the source side (i.e.,update-in-place data structure) or the target side (i.e., append-onlydata structures) during the data loading and the bucket-levelcomparison, and the difference report may be finalized as the finalreport. In another embodiment, an initially generated difference report(which may be considered an intermediate report) may contain differences(i.e., transient differences) caused by in-flight transactions and/orthe dirty read of data due to rollback transactions. In a furtherembodiment, transient differences may be removed from the initiallygenerated difference report based on the bucket-level comparison.

FIG. 3 illustrates a second flowchart 300 depicting another exemplarymethod for verifying data consistency between update-in-place datastructures and append-only data structures containing change historiesassociated with the update-in-place data structures, according to anembodiment. At 302, data from an update-in-place data structure (e.g.,RDBMS) may be loaded to a first set of hash buckets in a processingplatform, such as a big data platform (e.g., a cluster of machinesrunning a big data processing framework). At 304, data from one or moreappend-only data structures (e.g., recovery log files). Data from theRDBMS and the append-only files may be respectively loaded into a firstset and a second set of hash buckets, as described above regarding steps202 and 204 in FIG. 2

At 306, a bucket-level comparison may be performed between the data inthe first set of hash buckets and the second set of has buckets, asdescribed above regarding step 206 in FIG. 2.

At 308, a determination may be made as to whether an update occurredduring the bucket-level comparison. An update may occur in theupdate-in-place data structure or in the append-only data structure. Inan embodiment, the determination may also include updates occurringduring loading the data from the update-in-place data structure and/orthe data from the append-only data structures.

If no update occurred, at 310, an initial difference report based on thebucket-level comparison (performed at 306, described above) may befinalized as the final difference report, which may be reported orotherwise made available for querying and/or review (see, e.g., step316, described below).

If an update occurred, transient differences may be identified andremoved from the initial difference report based on the bucket-levelcomparison (performed at 306, described above). The number of transientdifferences may be considerably smaller than the number of differencesidentified between the update-in-place data structure and theappend-only data structure in the bucket-level comparison.

At 312, transient differences caused by in-flight transactions may beidentified and removed from the difference report. Transient differencescaused by in-flight transactions may include unread/unprocessedincremental changes committed at the update-in-place data structureafter the start of loading data from the update-in-place data structureto the processing platform.

In an embodiment, unread/unprocessed incremental changes committed afterthe start of loading data from the update-in-place data structure may beread/processed if the incremental changes have a size (e.g., transactionduration) longer than the sum of (i) the longest maximum replicationend-to-end latency and (ii) the time of the longest-running transaction(in the data structures being compared).

In an embodiment, checksums for incremental changes may be calculated.If key values and row-based checksums for incremental changes matchcorresponding values of the update-in-place data structure (included inthe initial difference report), the difference associated with theincremental changes may be removed from the initial difference report.

In one embodiment, a Bloom filter may be used to check incrementalchanges having key values included in the initial difference report.

In one embodiment, differences in the initial difference report may besorted based on the row-based checksum values. A binary search may beused to confirm if incremental changes have the same row-based checksumvalues.

At 314, transient differences caused by rollback transactions may beidentified and removed from the difference report. In an embodiment,after transient differences caused by in-flight transactions are removed(during step 312), key values for the remaining transient differencesmay be used to re-retrieve rows (e.g., a row-by-row re-fetch) from theupdate-in-place data structure in a cursor stable (CS) isolation level(or higher isolation level). After the row-by-row re-fetch, a waitinginterval may pass before unread/unprocessed incremental changes(associated with the remaining transient differences) may be loaded/readand processed. The waiting interval may have a duration longer than thesum of (i) the longest maximum replication end-to-end latency and (ii)the time of the longest-running transaction (in the data structuresbeing compared).

In an embodiment, checksums for re-fetched keys may be calculated, andmay be used to identify transient differences. Incremental changes(associated with the re-fetched keys) may be transient differences ifthe key values and row-based checksums for the re-fetched keys match (a)the key values and row-based checksums of the append-only datastructures in the initial difference report; or (b) the key values andthe row-based checksums of the rows in the incremental changes. Suchtransient differences may be removed from the initial difference report.

At 316, a final difference report may be generated. The final differencereport may reflect differences between an update-in-place data structureand append-only data structures. The final difference report may omittransient differences that may have been identified and removed from aninitial difference report based on the bucket-level comparison describedabove. The final difference report may list persistent differencesbetween the compared update-in-place data structure and append-only datastructures. In an embodiment, the differences may persist on theprocessing platform, e.g., a big data platform. In an embodiment, thedifferences may be inserted into another data structure associated withthe source, update-in-place data structure (e.g., a different table inan RDBMS). In an embodiment, a user may query results of the finaldifference report on either side (i.e., at the update-in-place side,such as an RDBMS, or at the side associated with the append-only side,such as a big data platform).

The methods, computer products, and systems disclosed herein may enablea live comparison of update-in-place data structures and append-onlydata structures. For example, persistent differences may be detectedwhile data structures (e.g., RDBMS, append-only log files) are beingactively updated or appended. Embodiments disclosed herein may have lowimpact on update-in-place data structures (e.g., RDBMS) by utilizing lowcost, distributed processing and storage capabilities in a big dataprocessing platforms. For example, the embodiments disclosed herein mayhave minimal locks on rows of data in an update-in-place data structure.Data loading, data transformation, and data comparison may be chunkedinto smaller jobs and parallelism of jobs may be handled by datamanagement layers associated with a big data processing platform tomaximize throughput with failure tolerance. Embodiments disclosed hereinmay analyze and compare large volumes of data with high scalability.

One or more aspects of the embodiments disclosed herein may be providedas part of a cloud-based service.

In one embodiment, the data from the update-in-place data structurecomprises a first set of key values that corresponds to rows of data inthe update-in-place data structure, and loading the data from theupdate-in-place data structure to the first set of hash buckets is basedon a first set of hash values associated with the first set of keyvalues.

In one embodiment, the data from the append-only data structurescomprises a second set of key values that corresponds to rows of data inthe append-only data structures, and loading the data from theappend-only data structures to the second set of hash buckets is basedon a second set of hash values associated with the second set of keyvalues.

In one embodiment, first set of hash values and the second set of hashvalues are determined based on a common hash function.

In one embodiment, the bucket-level comparison is performed on bucketsfrom the first set of hash buckets sharing common hash values withbuckets from the second set of hash buckets.

In one embodiment, the data from the update-in-place data structureincludes a first set of checksum values that corresponds to rows of datain the update-in-place data structure, and the data from append-onlydata structures includes a second set of checksum values thatcorresponds to rows of data in the append-only data structures.

In one embodiment, the method further includes generating a first set ofchecksum values that corresponds to rows of data in the update-in-placedata structure and generating a second set of checksum values thatcorresponds to rows of data in the append-only data structures, andgenerating the first set of checksum values and generating the secondset of checksum values are based on a common function (i.e., a functioncommon to both sets of calculations). That is, the first set of checksumvalues and the second set of checksum values are calculated with thesame function.

In one embodiment, the report based on the bucket-level comparisonincludes a third set of key values corresponding to differences betweenthe update-in-place data structure and the append-only data structures,a set of difference types corresponding to the third set of key values,checksum values from the first set of checksum values corresponding tothe third set of key values, and checksum values from the second set ofchecksum values corresponding to the third set of key values.

In one embodiment, generating the report based on the bucket-levelcomparison includes determining an update occurred to theupdate-in-place data structure during the bucket-level comparison. In afurther embodiment, generating the report based on the bucket-levelcomparison includes identifying transient differences between theupdate-in-place data structure and the append-only data structures, withthe transient differences being differences caused by in-flighttransactions committed at the update-in-place data structure afterloading the data from the update-in-place data structure to the firstset of hash buckets in the processing platform, and removing thetransient differences from an intermediate report listing differencesbetween the update-in-place data structure and the append-only datastructures.

In another embodiment, generating the report based on the bucket-levelcomparison includes identifying transient differences between theupdate-in-place data structure and the append-only data structures, withthe transient differences are differences caused by rollbacktransactions committed at the update-in-place data structure afterloading the data from the update-in-place data structure to the firstset of hash buckets in the processing platform, and removing thetransient differences from an intermediate report listing differencesbetween the update-in-place data structure and the append-only datastructures.

In one embodiment, loading the data from the update-in-place datastructure to the first set of hash buckets and loading the data from theappend-only data structures to the second set of hash buckets isperformed in parallel.

In one embodiment, the bucket-level comparison is performed in parallel.

In one embodiment, the update-in-place data structure is a relationaldatabase management systems (RDBMS).

Embodiments disclosed and contemplated herein may be implemented and/orperformed by any type of computer, known or contemplated, regardless ofthe platform being suitable for storing and/or executing program code.

FIG. 4 depicts a schematic illustrating an example of a computing node.Computing node 10 is only one example of a suitable computing node andis not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the invention described herein.Regardless, computing node 10 is capable of being implemented and/orperforming any of the functionality set forth hereinabove.

In computing node 10 there is a computer system/server 12, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed computing environments that includeany of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote computer system storage media including memorystorage devices.

As shown in FIG. 4, computer system/server 12 in computing node 10 isshown in the form of a general-purpose computing device. The componentsof computer system/server 12 may include, but are not limited to, one ormore processors or processing units 16, a system memory 28, and a bus 18that couples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or more(cloud) computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and data consistency verification processing96.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

References in the specification to “one embodiment”, “an embodiment”,“an example embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer program product for verifying dataconsistency between update-in-place data structures and append-only datastructures containing change histories associated with theupdate-in-place data structures, comprising a non-transitory tangiblestorage device having program code embodied therewith, the program codeexecutable by a processor of a computer to perform a method, the methodcomprising: loading data, by the processor, from an update-in-place datastructure to a first set of hash buckets in a processing platform;loading data, by the processor, from append-only data structures to asecond set of hash buckets in the processing platform; performing abucket-level comparison, by the processor, between the data in the firstset of hash buckets and the data in the second set of has buckets;generating an initial report, by the processor, based on the bucketlevel comparison; determining, by the processor, an update occurredduring the bucket level comparison; removing from the initial report, bythe processor, transient differences between the update-in-place datastructure and the append-only data structures, wherein the transientdifferences comprise differences caused by rollback transactionscommitted at the update-in-place data structure after loading the datafrom the update-in-place data structure to the first set of hash bucketsin the processing platform, and wherein the removing comprises arow-by-row re-fetch from the update-in-place data structure in anisolation level higher than a cursor stable isolation level; andgenerating a final report, by the processor, based on the initial reportand removal of the transient differences.